Affective computing, which involves systems that are capable of recognizing and analyzing the expression of human emotions, has become a flourishing area of research. Currently, most of the affective computing systems are still research-grade endeavors that are usually not robust enough to handle the demands of real world applications. However, the continuing increase in computing power coupled with the miniaturization of sensors and devices is making widespread adoption of affective computing systems in real world, day-to-day situations, closer than ever.
One capability that is useful for many affective computing systems is the ability to determine a user's expected response to a stimulus. While currently there are some existing systems for measuring user response to stimuli, they are inadequate when it comes to real world applications. The experimental data they collect is typically generated in a controlled environment. In these laboratory-like settings, a small number of short experiments are conducted (typically less than an hour long), in which a user's reactions are measured to a set of pre-selected stimuli, such as pictures, video scenes, or music. One main drawback of the laboratory-collected data is that it is acquired over a short period of time, in which the user is exposed only once to a stimulus, or even multiple times, but within a short duration. However, in real world scenarios, a user is likely to be exposed to stimuli multiple times over long periods. For example, a commercial broadcast in a campaign that runs for a few weeks, or a service robot that interacts with a user on a daily basis. When a user is exposed to similar stimuli multiple times over a duration of time, the user's reaction to the stimuli can be greatly influenced by a psychological phenomenon called habituation. Habituation is a wide spread phenomenon, which is detected with virtually all animal species, in which there is a decrease in the psychological and behavioral responses to a stimulus after repeated exposure to that stimulus over a duration of time. It is often difficult to predict to what extent, or at what rate, habituation will influence a specific user's response to a stimulus. Thus, data from typical short duration experiments conducted in laboratory settings are usually not sufficient to model a user's response to stimuli accurately, since they cannot correctly account for the influence of habituation.
Another shortcoming that limits the ability of affective computing systems relying on laboratory-collected data to determine a user's expected response to stimuli accurately is due to the data being collected when the user is always in very similar situations. In reality, a user's reaction to stimuli can vary dramatically depending on the situation the user is in. For example, a user's affective response while driving in busy traffic might be quite different from the user's response when relaxing at home, even if exposed to the same stimuli in both situations.
Analyzing affective response data collected in real world scenarios poses new challenges, which do not arise with data collected in controlled laboratory-like situations. For example, while in the laboratory the user's response is usually measured for a single stimulus at a time, in the real world, the user is simultaneously exposed to many stimuli of different natures and originating from various sources. Given the many challenges and complications involved in the real world domain, a system designed to determine a user's expected response to stimuli accurately in real world scenarios should take into account the added complexity intrinsic to this domain, such as the effects of habituation.